Descriptive analysis is a type of data examination that can help you identify important patterns in your data. It’s one of the first steps in any social science research project, as it helps you get an overview of how your variables relate to each other and how they compare to previous research.
- Descriptive analysis is used to examine data.
- Descriptive analysis is used to describe the characteristics of a sample.
- Descriptive analysis, also known as descriptive statistics, includes mean, median and mode; standard deviation; range, variance and covariance; histogram and box plot.
- It can also be used to examine the characteristics of a population by using summary measures such as mean, median and mode; standard deviation; range/interquartile range (IQR).
Descriptive analysis is a type of data examination that can help you identify important patterns in your data. The process involves three stages.
- Stage 1: Data preparation – Collecting and organizing your data into an organized format.
- Stage 2: Data analysis – Examining the collected information to see if there are any interesting trends or patterns within it that you might want to explore further.
- Stage 3: Presentation of results – Communicating the findings of your descriptive analyses through charts and graphs, tables, etc., to other people interested in what you’ve discovered
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Stages of descriptive analysis
Descriptive analysis is used to examine data. It has the following three stages:
- The first stage is the hypothesis. This stage aims to narrow your focus, so you can see exactly what data is available and how it’s organized.
- The second stage is profiling. Once you’ve put together a list of possible variables and attributes that may be related to your phenomenon, it’s time to evaluate their usefulness in answering questions about your phenomenon. You’ll want to answer questions such as “Is this variable useful?” and “How will I measure the attribute?” without getting too bogged down in details at this point; later on, when making inferences from descriptive analyses, it’ll become more important to fulfill these requirements precisely than now.
- The third stage is comparing where we compare all of our hypotheses against each other based on what type of results they provide us with – regression coefficients being one example of this type (see below).
1st Stage: Hypothesis testing:
The hypothesis is the first stage of descriptive analysis. This statement is your hypothesis and is the first step in analyzing data. It should be a testable, falsifiable statement that can be proven or disproven. For example: “if I drink 4 cups of coffee every day for 30 days, then I will feel more alert.”
This might sound like an obvious thing to do when planning an experiment, but it’s not always as simple as it seems! You need to make sure that your hypothesis makes sense and can be tested, as well as be able to answer questions like: What happens if my hypothesis is wrong?
2nd Stage: Profiling
Profiling the sample variables is the second stage of descriptive analysis. The data may be organized in several ways for profiling, including:
- Frequency distribution tables or histograms (for categorical data)
- Box-and-whisker plots (for continuous variables)
3rd Stage: Comparing Samples
Comparing samples is the last stage of descriptive analysis.
- This stage can be performed before or after you run your first set of tests. The main idea behind this technique is to compare one sample with another and identify any patterns in your data, such as outliers or consistent values.
- Comparing samples is also a useful way to identify sampling bias (i.e., when the sample does not represent your target population). For example, suppose you only choose students from a particular high school for your study because it’s close to where you live. In that case, this could lead to an unrepresentative sample since there may be other high schools with different cultures and academic profiles providing access to college-level courses. You may need more than just two samples if there are multiple subgroups within each category (e.g., boys vs girls) that should all be accounted for comparisons between them not to be biased due solely on their gender differences alone.”
Identifying Sampling or Statistical bias with the help of descriptive analysis:
Descriptive analyses can help you find a sampling or statistical bias in your research that will mean you can’t trust your results.
When doing descriptive analysis, you’re looking at the data from your study to check if it matches what you expected. If there’s any discrepancy between the two, then it could indicate there’s an error somewhere. This is called sampling bias, which means some people were given different treatments than others or weren’t given enough time to respond to questions correctly.
If this happens during an experiment or survey, their answers aren’t representative of their group (maybe because they’re more likely to answer wrongly). It would alter any conclusions about them being right or wrong on average across all groups!
Limitations of Descriptive Analysis:
Descriptive analyses have several limitations you must be aware of when using them.
Descriptive analyses are useful for what they’re meant to do: describe. But, as a data scientist
or analyst, you know that the most important thing you can do is make predictions about what may or may not happen in the future.
Descriptive analysis isn’t a good way to do this because it doesn’t account for any underlying relationships between variables–it’s just looking at them individually and describing them as they are now.
To make decisions and test hypotheses with data, you need predictive models that consider relationships among variables (like linear regression). This will help make better forecasts about future events based on past behaviour.
You also need predictive modelling when using or analyzing data to make inferences about new circumstances where there isn’t enough information available yet but still want some insight into how well-suited your model is likely to perform under those conditions based on previous experience with similar situations; this helps guide decision making beyond just descriptive analytics alone!
Conclusion
We hope this post has helped you understand descriptive analysis and its benefits. If you have any questions or comments, please feel free to leave them below, and we’ll get back to you as soon as possible!